Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations340
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.7 KiB
Average record size in memory104.4 B

Variable types

Text1
Numeric7
Categorical5

Alerts

Age is highly overall correlated with MedicalFitnessScore and 3 other fieldsHigh correlation
AttritionRisk is highly overall correlated with CommandersAssessment and 4 other fieldsHigh correlation
CommandersAssessment is highly overall correlated with AttritionRisk and 4 other fieldsHigh correlation
LeadershipPotential is highly overall correlated with AttritionRisk and 4 other fieldsHigh correlation
MedicalFitnessScore is highly overall correlated with Age and 3 other fieldsHigh correlation
MissionSuccessRate is highly overall correlated with AttritionRisk and 4 other fieldsHigh correlation
PeerReviewScore is highly overall correlated with AttritionRisk and 4 other fieldsHigh correlation
PerformanceRating is highly overall correlated with AttritionRisk and 5 other fieldsHigh correlation
Rank is highly overall correlated with Age and 3 other fieldsHigh correlation
Specialization is highly overall correlated with RankHigh correlation
TrainingCoursesCompleted is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsOfService is highly overall correlated with Age and 3 other fieldsHigh correlation
PersonnelID has unique values Unique

Reproduction

Analysis started2025-09-13 08:35:13.450921
Analysis finished2025-09-13 08:35:20.187745
Duration6.74 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PersonnelID
Text

Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:20.530099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2380
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)100.0%

Sample

1st rowIAF3001
2nd rowIAF3002
3rd rowIAF3003
4th rowIAF3004
5th rowIAF3005
ValueCountFrequency (%)
iaf3005 1
 
0.3%
iaf3340 1
 
0.3%
iaf3001 1
 
0.3%
iaf3002 1
 
0.3%
iaf3325 1
 
0.3%
iaf3326 1
 
0.3%
iaf3327 1
 
0.3%
iaf3328 1
 
0.3%
iaf3329 1
 
0.3%
iaf3330 1
 
0.3%
Other values (330) 330
97.1%
2025-09-13T08:35:20.965624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 455
19.1%
I 340
14.3%
A 340
14.3%
F 340
14.3%
1 174
 
7.3%
2 174
 
7.3%
0 172
 
7.2%
4 65
 
2.7%
9 64
 
2.7%
8 64
 
2.7%
Other values (3) 192
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 455
19.1%
I 340
14.3%
A 340
14.3%
F 340
14.3%
1 174
 
7.3%
2 174
 
7.3%
0 172
 
7.2%
4 65
 
2.7%
9 64
 
2.7%
8 64
 
2.7%
Other values (3) 192
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 455
19.1%
I 340
14.3%
A 340
14.3%
F 340
14.3%
1 174
 
7.3%
2 174
 
7.3%
0 172
 
7.2%
4 65
 
2.7%
9 64
 
2.7%
8 64
 
2.7%
Other values (3) 192
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 455
19.1%
I 340
14.3%
A 340
14.3%
F 340
14.3%
1 174
 
7.3%
2 174
 
7.3%
0 172
 
7.2%
4 65
 
2.7%
9 64
 
2.7%
8 64
 
2.7%
Other values (3) 192
8.1%

Age
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.129412
Minimum23
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:21.080271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q128
median34
Q340
95-th percentile45
Maximum47
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8002052
Coefficient of variation (CV)0.19924765
Kurtosis-1.130301
Mean34.129412
Median Absolute Deviation (MAD)6
Skewness0.10818745
Sum11604
Variance46.24279
MonotonicityNot monotonic
2025-09-13T08:35:21.183570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
25 18
 
5.3%
26 17
 
5.0%
33 17
 
5.0%
35 17
 
5.0%
29 17
 
5.0%
39 17
 
5.0%
28 16
 
4.7%
36 15
 
4.4%
38 15
 
4.4%
27 15
 
4.4%
Other values (15) 176
51.8%
ValueCountFrequency (%)
23 11
3.2%
24 13
3.8%
25 18
5.3%
26 17
5.0%
27 15
4.4%
28 16
4.7%
29 17
5.0%
30 13
3.8%
31 14
4.1%
32 13
3.8%
ValueCountFrequency (%)
47 4
 
1.2%
46 11
3.2%
45 13
3.8%
44 10
2.9%
43 9
2.6%
42 12
3.5%
41 13
3.8%
40 15
4.4%
39 17
5.0%
38 15
4.4%

YearsOfService
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.129412
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:21.292527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q318
95-th percentile23
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8002052
Coefficient of variation (CV)0.56063767
Kurtosis-1.130301
Mean12.129412
Median Absolute Deviation (MAD)6
Skewness0.10818745
Sum4124
Variance46.24279
MonotonicityNot monotonic
2025-09-13T08:35:21.397480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 18
 
5.3%
4 17
 
5.0%
11 17
 
5.0%
13 17
 
5.0%
7 17
 
5.0%
17 17
 
5.0%
6 16
 
4.7%
14 15
 
4.4%
16 15
 
4.4%
5 15
 
4.4%
Other values (15) 176
51.8%
ValueCountFrequency (%)
1 11
3.2%
2 13
3.8%
3 18
5.3%
4 17
5.0%
5 15
4.4%
6 16
4.7%
7 17
5.0%
8 13
3.8%
9 14
4.1%
10 13
3.8%
ValueCountFrequency (%)
25 4
 
1.2%
24 11
3.2%
23 13
3.8%
22 10
2.9%
21 9
2.6%
20 12
3.5%
19 13
3.8%
18 15
4.4%
17 17
5.0%
16 15
4.4%

Rank
Categorical

High correlation 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Wing Commander
88 
Squadron Leader
80 
Flying Officer
68 
Flight Lieutenant
64 
Group Captain
40 

Length

Max length17
Median length15
Mean length14.682353
Min length13

Characters and Unicode

Total characters4992
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGroup Captain
2nd rowSquadron Leader
3rd rowSquadron Leader
4th rowFlying Officer
5th rowWing Commander

Common Values

ValueCountFrequency (%)
Wing Commander 88
25.9%
Squadron Leader 80
23.5%
Flying Officer 68
20.0%
Flight Lieutenant 64
18.8%
Group Captain 40
11.8%

Length

2025-09-13T08:35:21.522156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T08:35:21.626859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
wing 88
12.9%
commander 88
12.9%
squadron 80
11.8%
leader 80
11.8%
flying 68
10.0%
officer 68
10.0%
flight 64
9.4%
lieutenant 64
9.4%
group 40
5.9%
captain 40
5.9%

Most occurring characters

ValueCountFrequency (%)
n 492
 
9.9%
e 444
 
8.9%
a 392
 
7.9%
i 392
 
7.9%
r 356
 
7.1%
340
 
6.8%
d 248
 
5.0%
t 232
 
4.6%
g 220
 
4.4%
o 208
 
4.2%
Other values (16) 1668
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 492
 
9.9%
e 444
 
8.9%
a 392
 
7.9%
i 392
 
7.9%
r 356
 
7.1%
340
 
6.8%
d 248
 
5.0%
t 232
 
4.6%
g 220
 
4.4%
o 208
 
4.2%
Other values (16) 1668
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 492
 
9.9%
e 444
 
8.9%
a 392
 
7.9%
i 392
 
7.9%
r 356
 
7.1%
340
 
6.8%
d 248
 
5.0%
t 232
 
4.6%
g 220
 
4.4%
o 208
 
4.2%
Other values (16) 1668
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 492
 
9.9%
e 444
 
8.9%
a 392
 
7.9%
i 392
 
7.9%
r 356
 
7.1%
340
 
6.8%
d 248
 
5.0%
t 232
 
4.6%
g 220
 
4.4%
o 208
 
4.2%
Other values (16) 1668
33.4%

Specialization
Categorical

High correlation 

Distinct8
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Pilot
92 
Medical
62 
Engineer
60 
Ground Staff
60 
Admin
59 
Other values (3)
 
7

Length

Max length12
Median length8
Mean length7.15
Min length5

Characters and Unicode

Total characters2431
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st rowPilot
2nd rowPilot
3rd rowAdmin
4th rowGround Staff
5th rowPilot

Common Values

ValueCountFrequency (%)
Pilot 92
27.1%
Medical 62
18.2%
Engineer 60
17.6%
Ground Staff 60
17.6%
Admin 59
17.4%
pilot 5
 
1.5%
Data Analyst 1
 
0.3%
admin 1
 
0.3%

Length

2025-09-13T08:35:21.744886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T08:35:21.838222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pilot 97
24.2%
medical 62
15.5%
engineer 60
15.0%
ground 60
15.0%
staff 60
15.0%
admin 60
15.0%
data 1
 
0.2%
analyst 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 279
 
11.5%
n 241
 
9.9%
d 182
 
7.5%
e 182
 
7.5%
l 160
 
6.6%
t 159
 
6.5%
o 157
 
6.5%
a 126
 
5.2%
r 120
 
4.9%
f 120
 
4.9%
Other values (15) 705
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 279
 
11.5%
n 241
 
9.9%
d 182
 
7.5%
e 182
 
7.5%
l 160
 
6.6%
t 159
 
6.5%
o 157
 
6.5%
a 126
 
5.2%
r 120
 
4.9%
f 120
 
4.9%
Other values (15) 705
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 279
 
11.5%
n 241
 
9.9%
d 182
 
7.5%
e 182
 
7.5%
l 160
 
6.6%
t 159
 
6.5%
o 157
 
6.5%
a 126
 
5.2%
r 120
 
4.9%
f 120
 
4.9%
Other values (15) 705
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 279
 
11.5%
n 241
 
9.9%
d 182
 
7.5%
e 182
 
7.5%
l 160
 
6.6%
t 159
 
6.5%
o 157
 
6.5%
a 126
 
5.2%
r 120
 
4.9%
f 120
 
4.9%
Other values (15) 705
29.0%

PerformanceRating
Categorical

High correlation 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
5
98 
4
88 
3
77 
2
76 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters340
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1
2nd row4
3rd row3
4th row2
5th row5

Common Values

ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

Length

2025-09-13T08:35:21.963232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T08:35:22.039685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 98
28.8%
4 88
25.9%
3 77
22.6%
2 76
22.4%
1 1
 
0.3%

TrainingCoursesCompleted
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0441176
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:22.125094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q311.25
95-th percentile15
Maximum17
Range16
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation4.37084
Coefficient of variation (CV)0.54335854
Kurtosis-0.98770617
Mean8.0441176
Median Absolute Deviation (MAD)3
Skewness0.12316075
Sum2735
Variance19.104243
MonotonicityNot monotonic
2025-09-13T08:35:22.219274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 30
 
8.8%
8 29
 
8.5%
7 28
 
8.2%
6 27
 
7.9%
11 25
 
7.4%
9 24
 
7.1%
5 23
 
6.8%
12 23
 
6.8%
1 19
 
5.6%
3 19
 
5.6%
Other values (7) 93
27.4%
ValueCountFrequency (%)
1 19
5.6%
2 30
8.8%
3 19
5.6%
4 14
4.1%
5 23
6.8%
6 27
7.9%
7 28
8.2%
8 29
8.5%
9 24
7.1%
10 17
5.0%
ValueCountFrequency (%)
17 4
 
1.2%
16 11
 
3.2%
15 15
4.4%
14 16
4.7%
13 16
4.7%
12 23
6.8%
11 25
7.4%
10 17
5.0%
9 24
7.1%
8 29
8.5%

MissionSuccessRate
Real number (ℝ)

High correlation 

Distinct166
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.775176
Minimum23.66
Maximum99.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:22.337787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23.66
5-th percentile79.5
Q186.3
median92.05
Q396.925
95-th percentile99.3
Maximum99.8
Range76.14
Interquartile range (IQR)10.625

Descriptive statistics

Standard deviation7.4251188
Coefficient of variation (CV)0.081796799
Kurtosis18.145688
Mean90.775176
Median Absolute Deviation (MAD)5.2
Skewness-2.4062827
Sum30863.56
Variance55.13239
MonotonicityNot monotonic
2025-09-13T08:35:22.471833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.1 5
 
1.5%
97.9 5
 
1.5%
99.5 5
 
1.5%
82.8 4
 
1.2%
98 4
 
1.2%
87.2 4
 
1.2%
98.3 4
 
1.2%
99.4 4
 
1.2%
98.7 4
 
1.2%
98.8 4
 
1.2%
Other values (156) 297
87.4%
ValueCountFrequency (%)
23.66 1
 
0.3%
75.4 1
 
0.3%
77.5 1
 
0.3%
77.7 2
0.6%
78.2 1
 
0.3%
78.3 2
0.6%
78.9 3
0.9%
79.1 2
0.6%
79.3 1
 
0.3%
79.4 1
 
0.3%
ValueCountFrequency (%)
99.8 3
0.9%
99.7 1
 
0.3%
99.6 2
 
0.6%
99.5 5
1.5%
99.4 4
1.2%
99.3 3
0.9%
99.2 3
0.9%
99.1 4
1.2%
99 3
0.9%
98.9 2
 
0.6%

MedicalFitnessScore
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.414706
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:22.593834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile86
Q190
median94
Q397
95-th percentile99
Maximum100
Range80
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.8583391
Coefficient of variation (CV)0.062713243
Kurtosis71.60112
Mean93.414706
Median Absolute Deviation (MAD)3
Skewness-5.9145426
Sum31761
Variance34.320137
MonotonicityNot monotonic
2025-09-13T08:35:22.699167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
99 34
 
10.0%
98 32
 
9.4%
97 29
 
8.5%
92 29
 
8.5%
96 26
 
7.6%
91 24
 
7.1%
94 23
 
6.8%
95 23
 
6.8%
93 18
 
5.3%
90 17
 
5.0%
Other values (8) 85
25.0%
ValueCountFrequency (%)
20 1
 
0.3%
84 1
 
0.3%
85 11
 
3.2%
86 13
3.8%
87 11
 
3.2%
88 15
4.4%
89 17
5.0%
90 17
5.0%
91 24
7.1%
92 29
8.5%
ValueCountFrequency (%)
100 16
4.7%
99 34
10.0%
98 32
9.4%
97 29
8.5%
96 26
7.6%
95 23
6.8%
94 23
6.8%
93 18
5.3%
92 29
8.5%
91 24
7.1%

PeerReviewScore
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9758824
Minimum2.3
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:22.803016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile2.9
Q13.5
median4.1
Q34.6
95-th percentile4.9
Maximum5
Range2.7
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.67203349
Coefficient of variation (CV)0.16902751
Kurtosis-1.0805376
Mean3.9758824
Median Absolute Deviation (MAD)0.5
Skewness-0.34190714
Sum1351.8
Variance0.45162901
MonotonicityNot monotonic
2025-09-13T08:35:22.914628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4.8 25
 
7.4%
4.3 21
 
6.2%
4.4 21
 
6.2%
4.7 20
 
5.9%
4.6 19
 
5.6%
4.9 18
 
5.3%
3.7 18
 
5.3%
3.6 18
 
5.3%
3.8 18
 
5.3%
4.5 17
 
5.0%
Other values (15) 145
42.6%
ValueCountFrequency (%)
2.3 1
 
0.3%
2.7 5
 
1.5%
2.8 10
2.9%
2.9 17
5.0%
3 15
4.4%
3.1 16
4.7%
3.2 9
2.6%
3.3 3
 
0.9%
3.4 5
 
1.5%
3.5 9
2.6%
ValueCountFrequency (%)
5 5
 
1.5%
4.9 18
5.3%
4.8 25
7.4%
4.7 20
5.9%
4.6 19
5.6%
4.5 17
5.0%
4.4 21
6.2%
4.3 21
6.2%
4.2 14
4.1%
4.1 15
4.4%

CommandersAssessment
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9773529
Minimum2.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-09-13T08:35:23.025667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.7
Q13.3
median4.2
Q34.7
95-th percentile5
Maximum5
Range2.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.78258255
Coefficient of variation (CV)0.19675964
Kurtosis-1.3636249
Mean3.9773529
Median Absolute Deviation (MAD)0.7
Skewness-0.29499269
Sum1352.3
Variance0.61243545
MonotonicityNot monotonic
2025-09-13T08:35:23.136000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4.9 34
 
10.0%
4.8 25
 
7.4%
4.5 23
 
6.8%
4.7 21
 
6.2%
5 18
 
5.3%
3.6 17
 
5.0%
4.2 17
 
5.0%
3.5 16
 
4.7%
4.4 16
 
4.7%
3.4 16
 
4.7%
Other values (16) 137
40.3%
ValueCountFrequency (%)
2.5 5
 
1.5%
2.6 6
 
1.8%
2.7 9
2.6%
2.8 14
4.1%
2.9 14
4.1%
3 15
4.4%
3.1 8
2.4%
3.2 6
 
1.8%
3.3 15
4.4%
3.4 16
4.7%
ValueCountFrequency (%)
5 18
5.3%
4.9 34
10.0%
4.8 25
7.4%
4.7 21
6.2%
4.6 13
 
3.8%
4.5 23
6.8%
4.4 16
4.7%
4.3 12
 
3.5%
4.2 17
5.0%
4.1 6
 
1.8%

AttritionRisk
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Low
191 
High
76 
Medium
73 

Length

Max length6
Median length3
Mean length3.8676471
Min length3

Characters and Unicode

Total characters1315
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowMedium
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Low 191
56.2%
High 76
 
22.4%
Medium 73
 
21.5%

Length

2025-09-13T08:35:23.534193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T08:35:23.650868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 191
56.2%
high 76
 
22.4%
medium 73
 
21.5%

Most occurring characters

ValueCountFrequency (%)
L 191
14.5%
o 191
14.5%
w 191
14.5%
i 149
11.3%
H 76
 
5.8%
g 76
 
5.8%
h 76
 
5.8%
M 73
 
5.6%
e 73
 
5.6%
d 73
 
5.6%
Other values (2) 146
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 191
14.5%
o 191
14.5%
w 191
14.5%
i 149
11.3%
H 76
 
5.8%
g 76
 
5.8%
h 76
 
5.8%
M 73
 
5.6%
e 73
 
5.6%
d 73
 
5.6%
Other values (2) 146
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 191
14.5%
o 191
14.5%
w 191
14.5%
i 149
11.3%
H 76
 
5.8%
g 76
 
5.8%
h 76
 
5.8%
M 73
 
5.6%
e 73
 
5.6%
d 73
 
5.6%
Other values (2) 146
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 191
14.5%
o 191
14.5%
w 191
14.5%
i 149
11.3%
H 76
 
5.8%
g 76
 
5.8%
h 76
 
5.8%
M 73
 
5.6%
e 73
 
5.6%
d 73
 
5.6%
Other values (2) 146
11.1%

LeadershipPotential
Categorical

High correlation 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
High
182 
Medium
76 
Low
70 
high
 
6
low
 
6

Length

Max length6
Median length4
Mean length4.2235294
Min length3

Characters and Unicode

Total characters1436
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowMedium
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High 182
53.5%
Medium 76
22.4%
Low 70
 
20.6%
high 6
 
1.8%
low 6
 
1.8%

Length

2025-09-13T08:35:23.795664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-13T08:35:23.913917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 188
55.3%
medium 76
22.4%
low 76
22.4%

Most occurring characters

ValueCountFrequency (%)
i 264
18.4%
h 194
13.5%
g 188
13.1%
H 182
12.7%
M 76
 
5.3%
e 76
 
5.3%
d 76
 
5.3%
u 76
 
5.3%
m 76
 
5.3%
o 76
 
5.3%
Other values (3) 152
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 264
18.4%
h 194
13.5%
g 188
13.1%
H 182
12.7%
M 76
 
5.3%
e 76
 
5.3%
d 76
 
5.3%
u 76
 
5.3%
m 76
 
5.3%
o 76
 
5.3%
Other values (3) 152
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 264
18.4%
h 194
13.5%
g 188
13.1%
H 182
12.7%
M 76
 
5.3%
e 76
 
5.3%
d 76
 
5.3%
u 76
 
5.3%
m 76
 
5.3%
o 76
 
5.3%
Other values (3) 152
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 264
18.4%
h 194
13.5%
g 188
13.1%
H 182
12.7%
M 76
 
5.3%
e 76
 
5.3%
d 76
 
5.3%
u 76
 
5.3%
m 76
 
5.3%
o 76
 
5.3%
Other values (3) 152
10.6%

Interactions

2025-09-13T08:35:18.970094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:14.500331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.236463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.919453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.820786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.561992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.294004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.059086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:14.684910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.342586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.015237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.918024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.661244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.395667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.356361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:14.774602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.429723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.114224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.020535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.775611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.489572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.474279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:14.865950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.527434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.223907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.131008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.877880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.586490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.572160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:14.963356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.628321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.514052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.239321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.987006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.690111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.673462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.062302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.729051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.618848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.356989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.093567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.791484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:19.765323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.149614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:15.821830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:16.718913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:17.460331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.196342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-13T08:35:18.880598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-13T08:35:24.046046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAttritionRiskCommandersAssessmentLeadershipPotentialMedicalFitnessScoreMissionSuccessRatePeerReviewScorePerformanceRatingRankSpecializationTrainingCoursesCompletedYearsOfService
Age1.0000.3300.3070.239-0.8960.3140.3150.2790.8570.3860.9841.000
AttritionRisk0.3301.0000.9360.9750.0000.8900.9650.9790.3260.2110.3620.330
CommandersAssessment0.3070.9361.0000.657-0.1460.9880.9900.7990.3530.2210.3720.307
LeadershipPotential0.2390.9750.6571.0000.2190.6350.6790.7000.2310.2940.2620.239
MedicalFitnessScore-0.8960.000-0.1460.2191.000-0.152-0.1520.5780.4230.314-0.869-0.896
MissionSuccessRate0.3140.8900.9880.635-0.1521.0000.9900.8160.2850.1570.3800.314
PeerReviewScore0.3150.9650.9900.679-0.1520.9901.0000.9620.4110.2410.3820.315
PerformanceRating0.2790.9790.7990.7000.5780.8160.9621.0000.2720.1630.2990.279
Rank0.8570.3260.3530.2310.4230.2850.4110.2721.0000.5120.8510.857
Specialization0.3860.2110.2210.2940.3140.1570.2410.1630.5121.0000.3560.386
TrainingCoursesCompleted0.9840.3620.3720.262-0.8690.3800.3820.2990.8510.3561.0000.984
YearsOfService1.0000.3300.3070.239-0.8960.3140.3150.2790.8570.3860.9841.000

Missing values

2025-09-13T08:35:19.922250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-13T08:35:20.058460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PersonnelIDAgeYearsOfServiceRankSpecializationPerformanceRatingTrainingCoursesCompletedMissionSuccessRateMedicalFitnessScorePeerReviewScoreCommandersAssessmentAttritionRiskLeadershipPotential
0IAF30014523Group CaptainPilot11523.66202.33.3LowHigh
1IAF30023412Squadron LeaderPilot4891.50993.84.3LowHigh
2IAF30033311Squadron LeaderAdmin3788.10923.53.6MediumMedium
3IAF3004253Flying OfficerGround Staff2281.30953.13.0HighLow
4IAF30053816Wing CommanderPilot51199.00894.84.9LowHigh
5IAF3006319Flight LieutenantPilot4694.50974.34.5LowHigh
6IAF3007264Flying Officerpilot3385.201003.63.4MediumMedium
7IAF30084119Wing Commanderpilot21079.80912.92.8Highhigh
8IAF30093614Squadron LeaderPilot5997.50964.64.8LowHigh
9IAF3010297Flight LieutenantMedical4692.80984.14.2LowHigh
PersonnelIDAgeYearsOfServiceRankSpecializationPerformanceRatingTrainingCoursesCompletedMissionSuccessRateMedicalFitnessScorePeerReviewScoreCommandersAssessmentAttritionRiskLeadershipPotential
330IAF33313816Wing CommanderPilot2983.2893.13.0HighLow
331IAF3332319Flight LieutenantEngineer5698.3984.84.9LowHigh
332IAF33334119Wing CommanderAdmin41493.3884.34.4LowHigh
333IAF3334275Flying OfficerGround Staff3287.21003.63.4MediumMedium
334IAF33353614Squadron LeaderMedical2878.9902.92.8Highhigh
335IAF33363917Wing CommanderPilot51199.0934.95.0LowHigh
336IAF3337308Flight LieutenantEngineer4591.7974.14.2LowHigh
337IAF33384523Group CaptainAdmin31588.4863.83.5MediumMedium
338IAF3339242Flying OfficerGround Staff2180.1993.02.9HighLow
339IAF33403412Squadron LeaderMedical5897.9914.74.8LowHigh